---
title: "Dashboard"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    source: embed
    navbar:
      - { title: "🏠 Back to Homepage", href: "../index.html" }
---

```{r block1, include=FALSE}
  library(tidyverse)
  library(p8105.datasets)
  library(plotly)
  library(flexdashboard)
  library(dplyr)
  library(rnoaa)
  library(ggplot2)
```

```{r block2, include=FALSE, message=FALSE, fig.height=7.5, fig.width=12.5}
# Load pre-saved data
load("nynoaadat.RData")

# Clean and filter nydat
nydat <- nydat %>% drop_na()

# Clean nystationids
nystationids <- nystationids %>% drop_na()

# Clean and filter stations (also downsample for plot size)
stations <- stations %>% drop_na() %>% sample_n(100000)

```

### Spatial Distribution of NOAA GHCN Weather Stations by Elevation

```{r block3, message=FALSE, warning=FALSE, fig.height=7.5, fig.width=12.5}
  # Scatterplot of station locations with color representing elevation
  p1 <- ggplot(stations, aes(x = longitude, y = latitude, color = elevation)) +
        geom_point(alpha = 0.7, size = 0.35) +
        scale_color_viridis_c(option = "C") +  # Use a color gradient suitable for elevation
        labs(
             x = "Longitude",
             y = "Latitude",
             color = "Elevation (m)") +
        theme_minimal() +
        theme(plot.title = element_text(hjust = 0.5) )
  plotly::ggplotly(p1)
```

### Distribution of Weather Station Elevations Grouped by Recorded Element

```{r block4,message=FALSE, warning=FALSE, fig.height=7.5, fig.width=12.5}
  # Boxplot of elevation by element
  p2 <- ggplot(stations, aes(x = element, y = elevation)) +
        geom_boxplot(fill = "lightgreen", outlier.size = 0.5, outlier.alpha = 0.5) +
        labs(
             x = "Element",
             y = "Elevation (m)") +
        theme_minimal() +
        theme(
          plot.title = element_text(hjust = 0.5),
          axis.text.x = element_text(angle = 45, hjust = 1, size = 5) )
  plotly::ggplotly(p2)
```

### Temporal Coverage of Meteorological Elements in Global Station Records

```{r block5, message=FALSE, warning=FALSE, fig.height=7.5, fig.width=12.5}
  # Summarize data to get the number of stations recording each element by year
  yearly_counts <- stations %>%
                    group_by(first_year, element) %>%
                    summarise(station_count = n()) %>%
                    ungroup()
# Line plot of station counts by year for each element with no legend
  p3 <- ggplot(yearly_counts, aes(x = first_year, y = station_count, color = element, size=0.5)) +
        geom_line(size = 0.4) +
        labs(
             x = "Year",
             y = "Number of Stations") +
        theme_minimal() +
        theme(plot.title = element_text(hjust = 0.5),
              legend.position = "none")
  plotly::ggplotly(p3)
```